World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
10203
World Ranking
7111
National Ranking
3119

Olivier Pietquin publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Olivier Pietquin sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 261 publications — 65th percentile

65% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Olivier Pietquin D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Olivier Pietquin sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Olivier Pietquin is affiliated with Google in the United States and has a research focus primarily in the field of computer science. Their scholarly output spans several subfields including artificial intelligence, management science and operations research, signal processing, economics and econometrics, as well as control and systems engineering.

The scientist has contributed extensively to topics such as reinforcement learning in robotics, topic modeling, natural language processing techniques, advanced bandit algorithms research, music and audio processing, game theory and applications, and adversarial robustness in machine learning.

The following recent papers represent a selection of their published work:

  • AudioLM: A Language Modeling Approach to Audio Generation (2023), IEEE/ACM Transactions on Audio Speech and Language Processing
  • What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study (2020), arXiv (Cornell University)
  • Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision (2023), Transactions of the Association for Computational Linguistics
  • Primal Wasserstein Imitation Learning (2020), arXiv (Cornell University)
  • Munchausen Reinforcement Learning (2020), arXiv (Cornell University)

Collaborative work is a notable aspect of their research activity, with frequent coauthors including Matthieu Geist, Mathieu Laurière, Florian Strub, Julien Pérolat, and Romuald Élie.

The scientist's research has appeared often in venues such as arXiv (Cornell University), where the highest number of publications was recorded. Other frequent publication venues include the Proceedings of the AAAI Conference on Artificial Intelligence, HAL (Le Centre pour la Communication Scientifique Directe), IEEE/ACM Transactions on Audio Speech and Language Processing, and Transactions of the Association for Computational Linguistics.

Best Publications

  • Noisy Networks For Exploration

    Meire Fortunato;Mohammad Gheshlaghi Azar;Bilal Piot;Jacob Menick

  • Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

    Matej Vecerík;Todd Hester;Jonathan Scholz;Fumin Wang

  • Deep Q-learning from Demonstrations

    Todd Hester;Matej Vecerik;Olivier Pietquin;Marc Lanctot

  • Deep Q-learning From Demonstrations.

    Todd Hester;Matej Vecerík;Olivier Pietquin;Marc Lanctot

  • AudioLM: A Language Modeling Approach to Audio Generation

    Unknown

  • Modulating early visual processing by language

    Harm de Vries;Florian Strub;Jeremie Mary;Hugo Larochelle

  • GuessWhat?! Visual Object Discovery through Multi-modal Dialogue

    Harm de Vries;Florian Strub;Sarath Chandar;Olivier Pietquin

  • A probabilistic framework for dialog simulation and optimal strategy learning

    O. Pietquin;T. Dutoit

  • Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation

    Alexandre Bérard;Olivier Pietquin;Laurent Besacier;Christophe Servan

  • End-to-End Automatic Speech Translation of Audiobooks

    Alexandre Berard;Laurent Besacier;Ali Can Kocabiyikoglu;Olivier Pietquin

  • Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation

    Alexandre Berard;Olivier Pietquin;Christophe Servan;Laurent Besacier

  • Learning from Demonstrations for Real World Reinforcement Learning

    Todd Hester;Matej Vecerík;Olivier Pietquin;Marc Lanctot

  • Machine learning for spoken dialogue systems

    Oliver Lemon;Olivier Pietquin

  • A survey on metrics for the evaluation of user simulations

    Olivier Pietquin;Helen F. Hastie

  • Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision

    Unknown

  • Observe and Look Further: Achieving Consistent Performance on Atari

    Tobias Pohlen;Bilal Piot;Todd Hester;Mohammad Gheshlaghi Azar

  • End-to-end optimization of goal-driven and visually grounded dialogue systems

    Florian Strub;Harm de Vries;Jérémie Mary;Bilal Piot

  • Sample-efficient batch reinforcement learning for dialogue management optimization

    Olivier Pietquin;Matthieu Geist;Senthilkumar Chandramohan;Hervé Frezza-Buet

  • User Simulation in Dialogue Systems Using Inverse Reinforcement Learning.

    Senthilkumar Chandramohan;Matthieu Geist;Fabrice Lefèvre;Olivier Pietquin

  • Kalman temporal differences

    Matthieu Geist;Olivier Pietquin

  • Inverse Reinforcement Learning through Structured Classification

    Edouard Klein;Matthieu Geist;Bilal Piot;Olivier Pietquin

  • End-to-end optimization of goal-driven and visually grounded dialogue systems Harm de Vries

    Florian Strub;Harm de Vries;Jeremie Mary;Bilal Piot

Frequent Co-Authors

Aaron Courville
Aaron Courville University of Montreal
Rémi Munos
Rémi Munos French Institute for Research in Computer Science and Automation - INRIA
Laurent Besacier
Laurent Besacier Grenoble Alpes University
Oliver Lemon
Oliver Lemon Heriot-Watt University
Thierry Dutoit
Thierry Dutoit University of Mons
Hugo Larochelle
Hugo Larochelle Google (United States)
Steve Young
Steve Young University of Cambridge
Joel Z. Leibo
Joel Z. Leibo DeepMind (United Kingdom)
Marc Lanctot
Marc Lanctot DeepMind (United Kingdom)
Tom Schaul
Tom Schaul DeepMind (United Kingdom)

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